





from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from ..image_utils import load_image


if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__A							:			Optional[int]         =			logging.get_logger(__name__)


__A							:			Dict         =			Dict[str, Any]
__A							:			Optional[int]         =			List[Prediction]







@add_end_docstrings(__snake_case	)
class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     def __init__(    self				,     *snake_case_				,     **snake_case_							):
         super().__init__(*snake_case_				,     **snake_case_							)

         if self.framework == "tf":
             raise ValueError(F"The {self.__class__} is only available in PyTorch."							)

         requires_backends(self				,     'vision'							)
         self.check_model_type(
             dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()							)							)




     def        lowerCAmelCase__		(    self				,     **snake_case_							):
         _A     =							{}
         if "threshold" in kwargs:
             _A     =							kwargs['threshold']
         return {}, {}, postprocess_kwargs




     def __call__(    self				,     *snake_case_				,     **snake_case_							):
         return super().__call__(*snake_case_				,     **snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							load_image(snake_case_							)
         _A     =							torch.IntTensor([[image.height, image.width]]							)
         _A     =							self.image_processor(images=[image]				,     return_tensors='pt'							)
         if self.tokenizer is not None:
             _A     =							self.tokenizer(text=inputs['words']				,     boxes=inputs['boxes']				,     return_tensors='pt'							)
         _A     =							target_size
         return inputs




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							model_inputs.pop('target_size'							)
         _A     =							self.model(**snake_case_							)
         _A     =							outputs.__class__({'target_size': target_size, **outputs}							)
         if self.tokenizer is not None:
             _A     =							model_inputs['bbox']
         return model_outputs




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_=0.9							):
         _A     =							model_outputs['target_size']
         if self.tokenizer is not None:
             # This is a LayoutLMForTokenClassification variant.
             # The OCR got the boxes and the model classified the words.
             _A,			_A     =							target_size[0].tolist()

             def unnormalize(snake_case_							):
                 return self._get_bounding_box(
                     torch.Tensor(
                         [
                             (width * bbox[0] / 1000),
                             (height * bbox[1] / 1000),
                             (width * bbox[2] / 1000),
                             (height * bbox[3] / 1000),
                         ]							)							)

             _A,			_A     =							model_outputs['logits'].squeeze(0							).softmax(dim=-1							).max(dim=-1							)
             _A     =							[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
             _A     =							[unnormalize(snake_case_							) for bbox in model_outputs['bbox'].squeeze(0							)]
             _A     =							['score', 'label', 'box']
             _A     =							[dict(zip(snake_case_				,     snake_case_							)							) for vals in zip(scores.tolist()				,     snake_case_				,     snake_case_							) if vals[0] > threshold]
         else:
             # This is a regular ForObjectDetectionModel
             _A     =							self.image_processor.post_process_object_detection(snake_case_				,     snake_case_				,     snake_case_							)
             _A     =							raw_annotations[0]
             _A     =							raw_annotation['scores']
             _A     =							raw_annotation['labels']
             _A     =							raw_annotation['boxes']

             _A     =							scores.tolist()
             _A     =							[self.model.config.idalabel[label.item()] for label in labels]
             _A     =							[self._get_bounding_box(snake_case_							) for box in boxes]

             # {"scores": [...], ...} --> [{"score":x, ...}, ...]
             _A     =							['score', 'label', 'box']
             _A     =							[
                 dict(zip(snake_case_				,     snake_case_							)							)
                 for vals in zip(raw_annotation['scores']				,     raw_annotation['labels']				,     raw_annotation['boxes']							)
             ]

         return annotation




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         if self.framework != "pt":
             raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.'							)
         _A,			_A,			_A,			_A     =							box.int().tolist()
         _A     =							{
             'xmin': xmin,
             'ymin': ymin,
             'xmax': xmax,
             'ymax': ymax,
         }
         return bbox













"""simple docstring"""





from scipy.stats import pearsonr

import datasets


_SCREAMING_SNAKE_CASE   :							List[str]          =		'''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
'''


_SCREAMING_SNAKE_CASE   :							int          =		'''
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        [\'p-value\', \'pearsonr\']
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74
        >>> print(round(results[\'p-value\'], 2))
        0.15
'''


_SCREAMING_SNAKE_CASE   :							Dict          =		'''
@article{2020SciPy-NMeth,
author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
      Haberland, Matt and Reddy, Tyler and Cournapeau, David and
      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
      Kern, Robert and Larson, Eric and Carey, C J and
      Polat, Ilhan and Feng, Yu and Moore, Eric W. and
      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
      Harris, Charles R. and Archibald, Anne M. and
      Ribeiro, Antonio H. and Pedregosa, Fabian and
      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
      Computing in Python}},
journal = {Nature Methods},
year    = {2020},
volume  = {17},
pages   = {261--272},
adsurl  = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    , _KWARGS_DESCRIPTION						)
class 	a		(   datasets.Metric						):






    def   UpperCamelCase			(			self	:				Dict   )	->						Any:
      return datasets.MetricInfo(
          description=_DESCRIPTION			,		citation=_CITATION			,		inputs_description=_KWARGS_DESCRIPTION			,		features=datasets.Features(
              {
                  'predictions': datasets.Value('float'   ),
                  'references': datasets.Value('float'   ),
              }   )			,		reference_urls=['https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html']			,		)






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				Tuple=False   )	->						Tuple:
      if return_pvalue:
        lowerCamelCase_       	=		pearsonr(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
        return {"pearsonr": results[0], "p-value": results[1]}
      else:
        return {"pearsonr": float(pearsonr(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )[0]   )}




